CHOpinionMiner: An unsupervised system for Chinese opinion target extraction

Opinion target extraction (OTE) is an important task in fine-grained sentiment analysis field, which focuses on the identification of the targets of users' opinions or sentiments from online reviews. Existing approaches to OTE are mainly based on unsupervised rule-based methods or supervised machine learning methods. However, the latter needs a large number of labeled samples to train their models and lack of labeled corpus limits the research progress on OTE of Chinese. In this paper, we proposed a novel unsupervised and domain independent system called CHOpinionMiner. First, noun phrases are extracted as candidate opinion targets based on phrase structure grammar, then the syntactic paths of the candidate opinion targets and the opinion words in one opinionated sentence are extracted. After that, the legal paths, or rather, the opinion targets-opinion lexicon pairs that are syntactically associated are selected according to the syntactic rules. Finally, not only the formal targets but also the orientations are obtained. Experiments on data sets consisting of microblog topics and product reviews demonstrate that our approach outperforms the existing state-of-the-art methods.